Methods and Systems for Detecting Shading for Solar Trackers

2. The method of claim 1 further comprising a learning algorithm configured to predict a position of the sun, the learning algorithm trained by training data and the measured currents from the plurality of PV strings, the training data comprising at least one of a time of year, a geography, and a plurality of sun positions stored in a memory, yielding a stored time of year, geography, and plurality of sun positions.

3. The method of claim 2, wherein the determining includes using the learning algorithm, and wherein the learning algorithm comprises a machine learning algorithm.

4. The method of claim 3, wherein the machine learning algorithm uses, as learning data, at least one of the stored time of year, geography, and plurality of sun positions to predict in advance what portion of the plurality of PV strings may be shaded.

5. The method of claim 4, wherein the tracker controller tracks the Sun based on a prediction from the machine learning algorithm.

6. The method of claim 1, wherein the tilt angle of one or more of the plurality of PV strings is further changed based on detecting a maximum output power.

7. The method of claim 1, further comprising determining a priority between a maximum output power and the determining if any portion is shaded.

8. The method of claim 1, wherein the shading includes south-north shading.

9. The method of claim 1, wherein the shading includes east-west shading.

10. The method of claim 1, wherein the shading includes south-north shading and east-west shading.